One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Integrated Population Models: Achieving Their Potential

Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.

A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia

BACKGROUND

Snakebite envenoming is a neglected tropical disease affecting deprived populations, and its burden is underestimated in some regions where patients prefer using traditional medicine, case reporting systems are deficient, or health systems are inaccessible to at-risk populations. Thus, the development of strategies to optimize disease management is a major challenge. We propose a framework that can be used to estimate total snakebite incidence at a fine political scale.

METHODOLOGY/PRINCIPAL FINDINGS

First, we generated fine-scale snakebite risk maps based on the distribution of venomous snakes in Colombia. We then used a generalized mixed-effect model that estimates total snakebite incidence based on risk maps, poverty, and travel time to the nearest medical center. Finally, we calibrated our model with snakebite data in Colombia from 2010 to 2019 using the Markov-chain-Monte-Carlo algorithm. Our results suggest that 10.19% of total snakebite cases (532.26 yearly envenomings) are not reported and these snakebite victims do not seek medical attention, and that populations in the Orinoco and Amazonian regions are the most at-risk and show the highest percentage of underreporting. We also found that variables such as precipitation of the driest month and mean temperature of the warmest quarter influences the suitability of environments for venomous snakes rather than absolute temperature or rainfall.

CONCLUSIONS/SIGNIFICANCE

Our framework permits snakebite underreporting to be estimated using data on snakebite incidence and surveillance, presence locations for the most medically significant venomous snake species, and openly available information on population size, poverty, climate, land cover, roads, and the locations of medical centers. Thus, our algorithm could be used in other countries to estimate total snakebite incidence and improve disease management strategies; however, this framework does not serve as a replacement for a surveillance system, which should be made a priority in countries facing similar public health challenges.

Ungulate spatiotemporal responses to contrasting predation risk from wolves and snow leopards

Spatial responses to risk from multiple predators can precipitate emergent consequences for prey (i.e. multiple-predator effects, MPEs) and mediate indirect interactions between predators. How prey navigate risk from multiple predators may therefore have important ramifications for understanding the propagation of predation-risk effects (PREs) through ecosystems. The interaction of predator and prey traits has emerged as a potentially key driver of antipredator behaviour but remains underexplored in large vertebrate systems, particularly where sympatric prey share multiple predators. We sought to better generalize our understanding of how predators influence their ecosystems by considering how multiple sources of contingency drive prey distribution in a multi-predator-multi-prey system. Specifically, we explored how two sympatric ungulates with different escape tactics-vertically agile, scrambling ibex Capra sibirica and sprinting argali Ovis ammon-responded to predation risk from shared predators with contrasting hunting modes-cursorial wolves Canis lupus and vertical-ambushing, stalking snow leopards Panthera uncia. Contrasting risk posed by the two predators presented prey with clear trade-offs. Ibex selected for greater exposure to chronic long-term risk from snow leopards, and argali for wolves, in a nearly symmetrical manner that was predictable based on the compatibility of their respective traits. Yet, acute short-term risk from the same predator upended these long-term strategies, increasing each ungulates' exposure to risk from the alternate predator in a manner consistent with a scenario in which conflicting antipredator behaviours precipitate risk-enhancing MPEs and mediate predator facilitation. By contrast, reactive responses to wolves led ibex to reduce their exposure to risk from both predators-a risk-reducing MPE. Evidence of a similar reactive risk-reducing effect for argali vis-à-vis snow leopards was lacking. Our results suggest that prey spatial responses and any resulting MPEs and prey-mediated interactions between predators are contingent on the interplay of hunting mode and escape tactics. Further investigation of interactions among various drivers of contingency in PREs will contribute to a more comprehensive understanding and improved forecasting of the ecological effects of predators.

Modeling the Effect of Cooperativity in Ternary Complex Formation and Targeted Protein Degradation Mediated by Heterobifunctional Degraders

Chemically induced proximity between certain endogenous enzymes and a protein of interest (POI) inside cells may cause post-translational modifications to the POI with biological consequences and potential therapeutic effects. Heterobifunctional (HBF) molecules that bind with one functional part to a target POI and with the other to an E3 ligase induce the formation of a target-HBF-E3 ternary complex, which can lead to ubiquitination and proteasomal degradation of the POI. Targeted protein degradation (TPD) by HBFs offers a promising approach to modulate disease-associated proteins, especially those that are intractable using other therapeutic approaches, such as enzymatic inhibition. The three-way interactions among the HBF, the target POI, and the ligase-including the protein-protein interaction between the POI and the ligase-contribute to the stability of the ternary complex, manifested as positive or negative binding cooperativity in its formation. How such cooperativity affects HBF-mediated degradation is an open question. In this work, we develop a pharmacodynamic model that describes the kinetics of the key reactions in the TPD process, and we use this model to investigate the role of cooperativity in the ternary complex formation and in the target POI degradation. Our model establishes the quantitative connection between the ternary complex stability and the degradation efficiency through the former's effect on the rate of catalytic turnover. We also develop a statistical inference model for determining cooperativity in intracellular ternary complex formation from cellular assay data and demonstrate it by quantifying the change in cooperativity due to site-directed mutagenesis at the POI-ligase interface of the SMARCA2-ACBI1-VHL ternary complex. Our pharmacodynamic model provides a quantitative framework to dissect the complex HBF-mediated TPD process and may inform the rational design of effective HBF degraders.

Monitoring partially marked populations using camera and telemetry data

Long-term monitoring is an important component of effective wildlife conservation. However, many methods for estimating density are too costly or difficult to implement over large spatial and temporal extents. Recently developed spatial mark-resight (SMR) models are increasingly being applied as a cost-effective method to estimate density when data include detections of both marked and unmarked individuals. We developed a generalized SMR model that can accommodate long-term camera data and auxiliary telemetry data for improved spatiotemporal inference in monitoring efforts. The model can be applied in two stages, with detection parameters estimated in the first stage using telemetry data and camera detections of instrumented individuals. Density is estimated in the second stage using camera data, with all individuals treated as unmarked. Serial correlation in detection and density parameters is accounted for using time-series models. The two-stage approach reduces computational demands and facilitates the application to large data sets from long-term monitoring initiatives. We applied the model to 3 years (2015-2017) of white-tailed deer (Odocoileus virginianus) data collected in three study areas of the Big Cypress Basin, Florida, USA. In total, 59 females marked with ear tags and fitted with GPS-telemetry collars were detected along with unmarked females on 180 remote cameras. Most of the temporal variation in density was driven by seasonal fluctuations, but one study area exhibited a slight population decline during the monitoring period. Modern technologies such as camera traps provide novel possibilities for long-term monitoring, but the resulting massive data sets, which are subject to unique sources of observation error, have posed analytical challenges. The two-stage spatial mark-resight framework provides a solution with lower computational demands than joint SMR models, allowing for easier implementation in practice. In addition, after detection parameters have been estimated, the model may be used to estimate density even if no synchronous auxiliary information on marked individuals is available, which is often the case in long-term monitoring.

Comparing the MCMC Efficiency of JAGS and Stan for the Multi-Level Intercept-Only Model in the Covariance- and Mean-Based and Classic Parametrization

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS family, such as JAGS, have been popular for over two decades. Recently, Stan entered the market with promises of higher efficiency fueled by advanced and more sophisticated algorithms. With this study, we want to contribute empirical results to the discussion about the sampling efficiency of JAGS and Stan. We conducted three simulation studies in which we varied the number of warmup iterations, the prior informativeness, and sample sizes and employed the multi-level intercept-only model in the covariance- and mean-based and in the classic parametrization. The target outcome was MCMC efficiency measured as effective sample size per second (ESS/s). Based on our specific (and limited) study setup, we found that (1) MCMC efficiency is much higher for the covariance- and mean-based parametrization than for the classic parametrization, (2) Stan clearly outperforms JAGS when the covariance- and mean-based parametrization is used, and that (3) JAGS clearly outperforms Stan when the classic parametrization is used.

Pollinator interaction flexibility across scales affects patch colonization and occupancy

Global change alters ecological communities and may disrupt ecological interactions and the provision of ecosystem functions. As ecological communities respond to global change, species may either go locally extinct or form novel interactions. To date, few studies have assessed how flexible species are in their interaction patterns, mainly due to the scarcity of data spanning long time series. Using a ten-year species-level dataset on the assembly of mutualistic networks from the Central Valley in California, we test whether interaction flexibility affects pollinators' colonization and persistence and their resulting habitat occupancy in a highly modified landscape. We propose three metrics of interaction flexibility associated with different scales of organization within ecological communities and explore which species' traits affect them. Our results provide empirical evidence linking species' ability to colonize habitat patches across a landscape to the role they play in networks. Phenological breadth and body size had contrasting effects on interaction flexibility. We demonstrate the relationship between mutualistic networks and species' ability to colonize and persist in the landscape, suggesting interaction flexibility as a potential mechanism for communities to maintain ecosystem function despite changes in community composition.